Comparability of Objective Structured Clinical Examinations (OSCEs) and Written Tests for Assessing Medical School Students’ Competencies: A Scoping Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective Structured Clinical Examinations (OSCEs) and written tests are commonly used to assess health professional students, but it remains unclear whether the additional human resources and expenses required for OSCEs, both in-person and online, are worthwhile for assessing competencies. This scoping review summarized literature identified by searching MEDLINE and EMBASE comparing 1) OSCEs and written tests and 2) in-person and online OSCEs, for assessing health professional trainees’ competencies. For Q1, 21 studies satisfied inclusion criteria. The most examined health profession was medical trainees (19, 90.5%), the comparison was most frequently OSCEs versus multiple-choice questions (MCQs) (18, 85.7%), and 18 (87.5%) examined the same competency domain. Most (77.5%) total score correlation coefficients between testing methods were weak ( r < 0.40). For Q2, 13 articles were included. In-person and online OSCEs were most used for medical trainees (9, 69.2%), checklists were the most prevalent evaluation scheme (7, 63.6%), and 14/17 overall score comparisons were not statistically significantly different. Generally low correlations exist between MCQ and OSCE scores, providing insufficient evidence as to whether OSCEs provide sufficient value to be worth their additional cost. Online OSCEs may be a viable alternative to in-person OSCEs for certain competencies where technical challenges can be met.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.041 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it